Recent earthquakes have caused extensive damage to large metropolitan areas. In 1989, the Loma Prieta earthquake caused selective damage to critical arteries in the transportation systems in the San Francisco Bay Area and underscored the need for rapid screening of structures and lifelines. The Northridge earthquake in 1995 caused significant problems with welded connections in steel frame buildings. The underlying damage was not manifested due to the architectural coverings of these buildings. Therefore, this potentially life-threatening damage went undetected for months exposing thousands to unnecessary risk. The Kobe earthquake in Japan in 1996 brought extensive damage to transportation and other lifelines throughout the area. In addition, there was a lack of action in the first 24 hours after the event.
Clearly, extreme events, i.e. natural disasters such as earthquakes, hurricanes, tornados and floods, can have serious negative impact on society by causing human suffering and economic losses. In addition, danger is also posed by long-term deterioration of large civil structures, especially on exposed skeletal structures such as bridges. A number of collapses and failures have occurred over the last few decades pointing to the need for the remote detection and diagnosis of bride structures.
With the significant negative impact that extreme events and long term deterioration can have on the built environment, monitoring of civil structures holds promise as a way to provide critical information for near real-time condition assessment. This information can be used in the prudent allocation of emergency response resources after earthquakes and for identification of incipient damage in structures experiencing longp-term deterioration. There is an economic and societal need to improve the response and condition assessment capabilities immediately following earthquakes and to extend the useful life of current infrastructure.
The vast majority of known work on monitoring civil structures has focused on developing algorithms to advance the detection and diagnosis of damage to structures. Little work has been done, however, to advance the hardware platform used to gather the data used by these algorithms. Many of the existing monitoring algorithms and strategies assume a sophisticated hardware infrastructure that has large up-front cost, low cost to benefit ratio, costly system installation, and expensive life-cycle operation and maintenance.
In particular, the current state-of-the-art hardware systems for monitoring structures use cables to transmit sensor signal data to a central unit. Monitoring civil structures, however, involves many long lengths of cable to cover the large spatial distances. In addition to being expensive to install, these cables can fail due to exposure to the environment or potential damage during extreme events. Long cables also result in sensor signal degradation. Current monitoring systems are not capable of providing rapid condition screening of structures exposed to extreme events in near real time, i.e., in tens of minutes. Known analysis procedures for long-term monitoring are limited to two classes, either modal or physical engineering parameter based, such as strain or corrosion sensing. Monitoring strategies in the past have focused largely on one class or the other. Both have shown promise in laboratory experiments but fall short when applied to generic real world civil structures.
The most common monitoring system is the heating, ventilation, and air conditioning system (HVAC) found in some homes and most commercial buildings. This monitoring system acquires data from its sensors, thermostats, and then adjusts the temperature of the forced air in the room through a simple threshold analysis technique. Other examples include home and office security systems. These monitoring systems acquire data from the passive infrared devices in rooms and contact surfaces near doors and windows to determine if a person has entered the area or is attempting to enter the area. In each case, the core characteristics are the ability to sense an external physical quantity, temperature, humidity, heat, or contact, and to perform useful actions or make useful analysis for presentation to the user. In the case of monitoring civil structures, however, the degree of sophistication in the instrumentation and the computational and data processing needs are far greater due to the increased number of instruments and the algorithms used.
Today's monitoring systems for civil structures are extensions of the laboratory based instrumentation systems. As shown in FIG. 1, they are characterized as having centralized data acquisition device 10 connected to various sensors 12 through cables 14. The cables are usually shielded coaxial cable. The most commonly used sensor for monitoring vibrations in civil structures is the accelerometer. The accelerometer is a transducer that converts the local acceleration field into an electrical signal proportional to the input acceleration. Common sensor designs are based on the use of piezo-electric, piezo-resistive, force-balance, and capacitive principles. These accelerometers output an analog signal that needs to be sampled and digitized for use in modern data processing systems. The conduit between the sensor and the data acquisition is usually, in the case of civil structures, a long shielded cable. The length of this cable can range from 10 to 1000 feet in practice. For a large number of sensor locations, the cabling requirements scale quite poorly. With all of the analog signals available at the centralized data acquisition system, an analog to digital converter is used to discretize the analog waveforms. The discretized data is then processed and archived for later analysis.
For systems with greater than 16 channels, it is common to have a data acquisition mainframe that accepts several cards or modules for acquiring groups of 8 or 16 channels at once. The mainframe will hold several of these cards giving the entire system a channel count near 64 channels. The cost of the mainframe and the incremental cost of each additional card or module represent step increases in cost and capacity limits for the monitoring system. For example, a mainframe with four 8-channel data acquisition modules can acquire 32 channels of data simultaneously. The need to add one more channel requires the purchase of an additional 8-channel module, representing approximately a 20% increase in cost.
Due to the wide application of these instrumentation systems, they are designed with maximum data sampling rates in the ten's of kHz. These rates are appropriate for applications in automotive and aerospace industries, but well above the need of monitoring civil structures for global vibrational quantities. With sampling rates in the ten's of kHz and significant channel counts a high bandwidth bus is necessary between the data acquisition mainframe and the data storage devices. Traditionally, this has also required the use of a Unix based workstation for data transmission and analysis. These instrumentation schemes are appropriate for controlled and fixed laboratory settings. Operating these systems in the field creates obstacles and problems for which the instrumentation was not originally designed.
The wealth of sophisticated hardware in these systems places great requirements on the data acquisition software. Fortunately, instrumentation vendors such as Hewlett Packard provide their own data acquisition and storage software. This software runs from the Unix workstation and configures the data acquisition mainframe and coordinates the transfer and storage of the acquired data. This completes the instrumentation component of a monitoring system.
With the data archived in the storage devices of the Unix workstation, a number of analysis procedures can be done to process the data and extract meaningful results. For the periodic monitoring of civil structures, it is typical to perform a modal analysis of the recorded acceleration time histories. There are a number of commercial software packages available for modal analysis, each with their own merits. In many cases, the modal analysis is just the first step in the data analysis.
With modal analysis the resulting information are the modal frequencies, mode shapes, and modal damping. This information can be used as input to a variety of engineering analysis procedures from finite element models (FEM) to fatigue mechanisms to constitutive models. In addition, current modal information can be combined with previous information in a Bayesian framework to make better estimates of probable damage locations and severity. In general, the use of these analysis procedures requires a great amount of familiarity with the specific structure and engineering judgement in the analysis process.
At this stage, the user of the monitoring system has taken raw digitized data and extracted the relevant engineering quantities from the data. This information can be used to improve the design and characteristics of the structure, infer locations of damage and deterioration, and calibrate mathematical models of the structure.
The application of conventional monitoring systems to civil structures creates many obstacles and problems. Perhaps the single largest problem is the installation of these systems. Maintenance and environmental exposure are also recurring issues.
Installation
Given a structure to monitor and the financial resources to purchase a monitoring system, the first task is to install the instrumentation on the structure. Initially, this process seems straightforward, simply attach the sensors to the structure. A closer look reveals the problems with conventional monitoring systems applied to civil structures: the labor cost of installing the units in difficult locations and then routing the necessary cabling back to the centralized data acquisition system. For large structures, the cost of installation approaches 25% of the total cost [Lee, 1997]. For a typical modal analysis the installation time, whether for a building or bridge, consumes over 75% of the total testing time. In existing buildings, the installation process is complicated by the need to find adequate locations for the instrumentation and conduits through which to wire the system together. In bridges, the problem is further complicated by the skeletal nature of these structures. There is often no existing conduit or obvious way to route cabling [Farrar, 1996].
Maintenance
With the monitoring system installed, the concern shifts to the cost of maintenance and required readiness of the system. For bridges, the main concern and consequent cost is the exposure of the sensors and cabling to the external environment. Specifically, the repeated changes in temperature and humidity, and exposure to corrosive and direct sunlight significantly speed the degradation of sensors and cables. Given the already high cost of installation, returning to the structure on a regular basis to replace cables and sensors presents a formidable barrier to the practical application of these systems. For buildings, the problem is not the exposure to the external environment but to the other denizens of the building, namely rodents [Nigbor, 1997]. The coaxial cables of a conventional monitoring system that run in the conduits within a building can be damaged by rodents. A monitoring system designed with civil structures in mind should focus on mitigating the maintenance issues thereby increasing the reliability.
Additional Constraints
There are two additional constraints that conventional monitoring systems present. These problems are not specific to the civil monitoring scenario but to the architecture of the system itself. Often in experimental testing of structures, there is the desire to add several more channels of instrumentation after the initial installation. With a conventional instrumentation system, the desire to add one more channel to a system of 32 channels may require the purchase of an entire data acquisition module for the mainframe. This is the "One more Channel" situation. In effect, there is no ability to add capacity to the monitoring system.
One operational constraint is the long distances over which the analog signals travel. In industrial settings or exposure to thermal gradients, the analog signals may become noisy and degrade due to coupled noise sources near the cable path. Finally, given the nature of the instrumentation and the Unix workstation needed to transfer the data from the mainframe usually via a bus interface, the cost of these components has not decreased appreciably when compared to PC hardware.